An assessment of the use of RADARSAT-2 for detailed topographic mapping in a tropical semiarid terrain of Brazil
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Bibliographic record
Abstract
In this paper, the feasibility of using planialtimetric information derived from RADARSAT-2 (RST-2) ultra-fine (UF) stereo pairs and fine quad-pol (FQP) images for detailed topographic mapping was investigated for a semiarid terrain in the Curaçá Valley, northeast of Brazil. Precise topographic field information acquired from a global positioning system was used for ground control points for the modeling of the stereoscopic digital surface models (DSMs), ortho-images, and as independent check points for the calculation of planialtimetric accuracies. The analysis was performed with the following two approaches: (i) the use of root mean square error for the overall classification of the DSMs and ortho-images considering the Brazilian Map Accuracy Standard limits, and (ii) calculations of systematic errors (bias) and accuracy based on a methodology that takes into account computed discrepancies and standard deviations. Thematic information was extracted from FQP data through the use of an unsupervised terrain and land-use classification scheme based on the Freeman–Durden decomposition and the Wishart classifier. The investigation showed that the planialtimetric accuracies of UF DSMs and ortho-images and the thematic information of the FQP data fulfilled the requirements compatible to detailed topographic mapping (1:50000). Thus, the use of RST-2 data can be considered a real alternative as a primary source for detailed topographic mapping programs in similar environments of Brazil, where terrain information is limited or of a poor quality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it